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---
language:
- en
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
task_categories:
- feature-extraction
- sentence-similarity
pretty_name: NLI for SimCSE
tags:
- sentence-transformers
dataset_info:
- config_name: triplet
features:
- name: anchor
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
splits:
- name: train
num_bytes: 51033641
num_examples: 274951
download_size: 33517191
dataset_size: 51033641
- config_name: triplet-7
features:
- name: anchor
dtype: string
- name: positive
dtype: string
- name: negative_1
dtype: string
- name: negative_2
dtype: string
- name: negative_3
dtype: string
- name: negative_4
dtype: string
- name: negative_5
dtype: string
- name: negative_6
dtype: string
- name: negative_7
dtype: string
splits:
- name: train
num_bytes: 129065964
num_examples: 273540
download_size: 87886620
dataset_size: 129065964
- config_name: triplet-all
features:
- name: anchor
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
splits:
- name: train
num_bytes: 357145333
num_examples: 1925996
download_size: 94616052
dataset_size: 357145333
configs:
- config_name: triplet
data_files:
- split: train
path: triplet/train-*
- config_name: triplet-7
data_files:
- split: train
path: triplet-7/train-*
- config_name: triplet-all
data_files:
- split: train
path: triplet-all/train-*
---
# Dataset Card for NLI for SimCSE
This is a reformatting of the NLI for SimCSE Dataset used to train the [BGE-M3 model](https://huggingface.co/BAAI/bge-m3). See the full BGE-M3 dataset in [Shitao/bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data).
Despite being labeled as Natural Language Inference (NLI), this dataset can be used for training/finetuning an embedding model for semantic textual similarity.
## Dataset Subsets
### `triplet` subset
* Columns: "anchor", "positive", "negative"
* Column types: `str`, `str`, `str`
* Examples:
```python
{
'anchor': 'One of our number will carry out your instructions minutely.',
'positive': 'A member of my team will execute your orders with immense precision.',
'negative': 'We have no one free at the moment so you have to take action yourself.'
}
```
* Collection strategy: Reading the jsonl file in the `en_NLI_data` directory in [Shitao/bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) and taking only the first negative.
* Deduplified: No
### `triplet-7` subset
* Columns: "anchor", "positive", "negative_1", "negative_2", "negative_3", "negative_4", "negative_5", "negative_6", "negative_7"
* Column types: `str`, `str`, `str`, `str`, `str`, `str`, `str`
* Examples:
```python
{
'anchor': 'One of our number will carry out your instructions minutely.',
'positive': 'A member of my team will execute your orders with immense precision.',
'negative_1': 'We have no one free at the moment so you have to take action yourself.',
'negative_2': 'A poodle is running through the grass.',
'negative_3': 'Investment and planning are growing industries in Jamaica.',
'negative_4': 'A bearded man is rocking out on an acoustic guitar',
'negative_5': 'The people are sunbathing on the beach.',
'negative_6': 'A construction worker installs a door.',
'negative_7': 'A crowd has gathered because of a dangerous situation.'
}
```
* Collection strategy: Reading the jsonl file in the `en_NLI_data` directory in [Shitao/bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) and taking all samples that have 7 negatives (which is by far the majority).
* Deduplified: No
### `triplet-all` subset
* Columns: "anchor", "positive", "negative"
* Column types: `str`, `str`, `str`
* Examples:
```python
{
'anchor': 'One of our number will carry out your instructions minutely.',
'positive': 'A member of my team will execute your orders with immense precision.',
'negative': 'We have no one free at the moment so you have to take action yourself.'
}
```
* Collection strategy: Reading the jsonl file in the `en_NLI_data` directory in [Shitao/bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) and taking each negative, but making a separate sample with each of the negatives.
* Deduplified: No |